code: def test_create_model_with_optional_params(
      sagemaker_session, coach_mxnet_version
  ):
      container_log_level = '"logging.INFO"'
      source_dir = "s3://mybucket/source"
      rl = RLEstimator(
          entry_point=SCRIPT_PATH,
          role=ROLE,
          sagemaker_session=sagemaker_session,
          instance_count=INSTANCE_COUNT,
          instance_type=INSTANCE_TYPE,
          toolkit=RLToolkit.COACH,
          toolkit_version=coach_mxnet_version,
          framework=RLFramework.MXNET,
          container_log_level=container_log_level,
          source_dir=source_dir,
      )
  
      rl.fit(job_name="new_name")
  
      new_role = "role"
      new_entry_point = "deploy_script.py"
      vpc_config = {"Subnets": ["foo"], "SecurityGroupIds": ["bar"]}
      model_name = "model-name"
      model = rl.create_model(
          role=new_role,
          entry_point=new_entry_point,
          vpc_config_override=vpc_config,
          name=model_name,
      )
  
      assert model.role == new_role
      assert model.vpc_config == vpc_config
      assert model.entry_point == new_entry_point
      assert model.name == model_name
  
imports: from __future__ import absolute_import
  import json
  import logging
  import os
  import pytest
  from mock import MagicMock, Mock, patch
  from sagemaker.mxnet import MXNetModel, MXNetPredictor
  from sagemaker.rl import RLEstimator, RLFramework, RLToolkit, TOOLKIT_FRAMEWORK_VERSION_MAP
  from sagemaker.session_settings import SessionSettings
  from sagemaker.tensorflow import TensorFlowModel, TensorFlowPredictor
label_desc: Use image node_id 7 for calling a global function with example usage: test_create_model_with_optional_params(sagemaker_session, coach_mxnet_version) without return types